While reviewing the literature, we found a gap between research on TL and DA for classification and regression purposes, where research is mainly tailored towards TL and DA for classification. Especially within the RS community, research combining TL, DA, and regression is even more absent. The review paper of Tuia et al. [1]} gives an overview of DA methods in RS, but focuses only on classification. Some of the papers reviewed in [1]} apply simple ML techniques for the process, but none focus on the possibilities with DL. Neither of two comprehensive surveys on TL published in 2010 [3]} or 2016 [4]} considers image translation and generative models such as GANs as tools within TL or DA. Zhang et al. [5]} were in 2019 the first to categorise image translation through GANs or cGANS as unsupervised heterogeneous domain adaptation (UHDA). Within UHDA, it is assumed that no labelled target data are available and that the feature spaces could be completely different between the two datasets in both domains. This is also the case for the proposed work in this paper. UDHA methods aim to find a proper translation between the two domains, such that the target task can be boosted by knowledge from the source domain data [5]}.
